class_docstr: A regularizer that applies both L1 and L2 regularization penalties.
  
  The L1 regularization penalty is computed as:
  `loss = l1 * reduce_sum(abs(x))`
  
  The L2 regularization penalty is computed as
  `loss = l2 * reduce_sum(square(x))`
  
  L1L2 may be passed to a layer as a string identifier:
  
  >>> dense = Dense(3, kernel_regularizer='l1_l2')
  
  In this case, the default values used are `l1=0.01` and `l2=0.01`.
  
  Arguments:
      l1: float, L1 regularization factor.
      l2: float, L2 regularization factor.
code: def get_config(self):
      return {"l1": float(self.l1), "l2": float(self.l2)}
  
imports: import math
  from keras import ops
  from keras.api_export import keras_export
  from keras.utils.numerical_utils import normalize
label_desc: Use image node_id 3 for calling the L1L2 obj's underlying member method code with example usage: obj.get_config() and returns: dict